Methods for traffic scheduling at intersections in smart cities and internet of things (iot) systems thereof

ABSTRACT

The present disclosure provides a method for traffic scheduling at an intersection in a smart city and an Internet of Things system. The method includes: for the intersection in a preset area, determining whether traffic congestion is likely to occur at the intersection during a next time period based on a comparison result between the number of the first vehicles and the number of second vehicles; in response to determining that the traffic congestion is likely to occur at the intersection during the next time period, determining whether a traffic scheduling strategy is needed to be switched based on traffic data information of the intersection during the next time period; and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/660,024, filed on Apr. 21, 2022, which claims priority to ChinesePatent Application No. 202210321872.2 filed on Mar. 29, 2022, thecontents of which are hereby incorporated by reference to its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of Internet of Things (IoT)and cloud platforms, and in particular, to methods for trafficscheduling at intersections in smart cities and IoT systems.

BACKGROUND

Traffic congestion is often caused by traffic accidents, road occupationconstruction, special vehicle access, special transportation, etc. Inorder to alleviate the traffic congestion, traffic managers often needto formulate different traffic scheduling strategies. A process offormulating traffic scheduling strategies usually depends on humanexperience, which is easy to cause problems such as unscientificjudgment, poor accuracy, and failure to respond in time.

Therefore, it is necessary to provide methods for traffic scheduling atintersections in smart cities and Internet of Things (IoT) systems toensure scientific formulation of traffic scheduling strategies, so as toimprove efficiency and quality of traffic management.

SUMMARY

One aspect of some embodiments of the present disclosure provides amethod for traffic scheduling at an intersection in a smart city. Themethod may be executed by a traffic scheduling strategy controlmanagement platform. The method may comprise: determining, based on aroad monitoring video of the intersection in a preset area before acurrent time, a first average speed of each vehicle among a plurality ofvehicles on one or more roads connected with the intersection before thecurrent time; determining a number of first vehicles, the first averagespeed of each first vehicle being less than a first preset threshold;obtaining the road monitoring video of the intersection at the currenttime when the number of the first vehicles is greater than a secondpreset threshold; obtaining, based on the road monitoring video at thecurrent time, a second average speed of each vehicle among the pluralityof vehicles at the current time; determining a number of secondvehicles, the second average speed of each second vehicle being lessthan the first preset threshold; determining whether traffic congestionis likely to occur at the intersection during a next time period basedon a comparison result obtained by comparing the number of the firstvehicles with the number of second vehicles; in response to determiningthat the traffic congestion is likely to occur at the intersectionduring the next time period, determining whether a traffic schedulingstrategy is needed to be switched based on traffic data information ofthe intersection during the next time period; and in response todetermining that the traffic scheduling strategy is needed to beswitched, switching a first traffic scheduling strategy to a secondtraffic scheduling strategy.

One aspect of some embodiments of the present disclosure provides anInternet of Things (IoT) system for traffic scheduling at anintersection in a smart city. The IoT system may comprise a userplatform, a service platform, a traffic scheduling strategy controlmanagement platform, a sensor network platform, and an object platforminteracting in sequence. The traffic scheduling strategy controlmanagement platform is configured to perform operations comprising:determining, based on a road monitoring video of the intersection in apreset area before a current time, a first average speed of each vehicleamong a plurality of vehicles on one or more roads connected with theintersection before the current time; determining a number of firstvehicles, the first average speed of each first vehicle being less thana first preset threshold; obtaining the road monitoring video of theintersection at the current time when the number of the first vehiclesis greater than a second preset threshold; obtaining, based on the roadmonitoring video at the current time, a second average speed of eachvehicle among the plurality of vehicles at the current time; determininga number of second vehicles, the second average speed of each secondvehicle being less than the first preset threshold; determining whethertraffic congestion is likely to occur at the intersection during a nexttime period based on a comparison result obtained by comparing thenumber of the first vehicles with the number of second vehicles; inresponse to determining that the traffic congestion is likely to occurat the intersection during the next time period, determining whether atraffic scheduling strategy is needed to be switched based on trafficdata information of the intersection during the next time period; and inresponse to determining that the traffic scheduling strategy is neededto be switched, switching a first traffic scheduling strategy to asecond traffic scheduling strategy.

Another aspect of some embodiments of the present disclosure provides anon-transitory computer readable storage medium storing a set ofinstructions. When the set of instructions are executed by at least oneprocessor, the at least one processor may perform a method for trafficscheduling at an intersection in a smart city.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary applicationscenario of an Internet of Things (IoT) system for controlling a trafficscheduling strategy in a smart city according to some embodiments of thepresent disclosure;

FIG. 2 is a schematic diagram illustrating an exemplary IoT system forcontrolling a traffic scheduling strategy in a smart city according tosome embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating an exemplary process of a method forcontrolling a traffic scheduling strategy in a smart city according tosome embodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary structure of atraffic state prediction model according to some embodiments of thepresent disclosure;

FIG. 5 is a schematic diagram illustrating an exemplary structure of atraffic scheduling strategy prediction model according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. Obviously, drawings described below are onlysome examples or embodiments of the present disclosure. Those skilled inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings. Itshould be understood that the purposes of these illustrated embodimentsare only provided to those skilled in the art to practice theapplication, and not intended to limit the scope of the presentdisclosure. Unless obviously obtained from the context or the contextillustrates otherwise, the same numeral in the drawings refers to thesame structure or operation.

It will be understood that the terms “system,” “device,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, sections, or assemblies of different levels. However,the terms may be displaced by another expression if they achieve thesame purpose.

The terminology used herein is for the purposes of describing particularexamples and embodiments only and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

FIG. 1 is a schematic diagram illustrating an exemplary applicationscenario of an Internet of Things (IoT) system for controlling a trafficscheduling strategy in a smart city according to some embodiments of thepresent disclosure. In some embodiments, an application scenario mayinclude a server 110, a storage device 120, a user terminal 130, asensing device 140, an IoT gateway 150, and a network 160.

In some embodiments, the server 110 may be a single server or a servergroup. The server group may be centralized or distributed. For example,the server 110 may be a distributed system. In some embodiments, theserver 110 may be local or remote. In some embodiments, the server 110may be implemented on a cloud platform. In some embodiments, the server110 or a portion of the server 110 may be integrated into the sensingdevice 140.

In some embodiments, the server 110 may include a processing device 112.The processing device 112 may be configured to obtain, analyze, andprocess information to perform one or more functions described in thepresent disclosure. For example, the processing device 112 may obtaintraffic data monitored by the sensing device 140 and determine areaswith traffic congestion. As another example, the processing device 112may, based on the areas with traffic congestion, generate a schedulingstrategy, issue a control instruction to a traffic scheduling center170, and control the traffic scheduling center 170 to perform trafficmanagement according to the traffic scheduling strategy.

In some embodiments, the processing device 112 may include one or moreprocessing engines (e.g., single-chip processing engines or multi-chipprocessing engines). As an example, the processing device 112 mayinclude a central processing unit (CPU), an application specificintegrated circuit (ASIC), or like, or any combination thereof.

The storage device 120 may be configured to store data and/orinstructions, for example, the storage device 120 may be configured tostore the traffic data monitored by the sensing device 140. The storagedevice 120 may obtain data and/or instructions from, for example, theserver 110, the user terminal 130, or the like. In some embodiments, thestorage device 120 may store data and/or instructions executed or usedby the processing device 112 to implement the exemplary methodsdescribed in the present disclosure.

The user terminal 130 may refer to a terminal used by the user forinputting traffic data information, querying traffic information,querying traffic scheduling strategies, or other information. Forexample, the user terminal 130 may include, but is not limited to, asmartphone 130-1, a tablet 130-2, a laptop 130-3, a processor 130-4,other devices with input and/or output functions, or any combinationthereof. In some embodiments, the user terminal 130 may be associatedwith the server 110. For example, the user terminal 130 may also feedback traffic congestion information. In some embodiments, the userterminal 130 may be one or more users, which may include users whodirectly use service or other related users.

The sensing device 140 may refer to a device for obtaining the trafficdata information. For example, the sensing device 140 may include, butis not limited to, a road monitoring device 140-1 and an Unmanned AerialVehicle (UAV) shooting device 140-2. In some embodiments, the roadmonitoring device 140-1 may be an infrared camera or a high-definitiondigital camera. In some embodiments, the UAV shooting device 140-2 maybe an unmanned aircraft operated by a radio remote control device. Forexample, the UAV shooting device 140-2 may include a multi-rotor UAV, anunmanned helicopter, a solar-powered UAV, or the like. In someembodiments, the sensing device 140 may be configured as one or moreobject sub-platforms of the IoT. The road monitoring device 140-1 may bea sub-platform of the road monitoring device, and the UAV shootingdevice 140-2 may be a sub-platform of the UAV shooting device.

The IoT gateway 150 may refer to a data channel and a gateway where theuser terminal 130 and/or the sensing device 140 upload monitoring data.For example, the IoT gateway 150 may include, but is not limited to, anIoT gateway of the road monitoring device 150-1, an IoT gateway of theUAV shooting device 150-2, and an IoT gateway of the user terminal150-3. In some embodiments, the road monitoring device 140-1 may uploadroad monitoring data through the IoT gateway of the road monitoringdevice 150-1. The UAV shooting device 140-2 may upload road monitoringdata through the IoT gateway of the UAV shooting device 150-2. The userterminal 130 may upload road monitoring data through the IoT gateway ofthe user terminal 150-3. In some embodiments, the server 110 may issuecontrol instructions to control operation of the road monitoring device140-1 through the IoT gateway of the road monitoring device 150-1. Insome embodiments, the server 110 may issue control instructions tocontrol operation of the UAV shooting device 140-2 through the IoTgateway of the UAV shooting device 150-2.

The network 160 may provide a channel for information and/or dataexchange. In some embodiments, the information may be exchanged amongthe server 110, the storage device 120, the user terminal 130, thesensing device 140, the IoT gateway 150, and the traffic schedulingcenter 170 through the network 160. For example, the server 110 mayreceive a request to query traffic data sent by the user terminal 130through the network 160. As another example, the server 110 may obtaintraffic monitoring data uploaded by the user terminal 130 and/or thesensing device 140 through the network 160 and store the trafficmonitoring data in the storage device 120. For another example, theserver 110 may obtain a feedback of the traffic scheduling center 170through the network 160.

The traffic scheduling center 170 may refer to a center for performingtraffic scheduling strategies. For example, the traffic schedulingcenter 170 may perform operations including, but not limited to, atemporary traffic control 170-1, a traffic police scheduling 170-2, or atraffic light adjustment 170-3. In some embodiments, the trafficscheduling center 170 may obtain the traffic scheduling strategy issuedby the server 110 to perform the traffic light adjustment 170-3. In someembodiments, the traffic scheduling center 170 may obtain the trafficscheduling strategy issued by the server 110 to perform the trafficpolice scheduling 170-2. In some embodiments, the traffic schedulingcenter 170 may obtain the traffic scheduling strategy issued by theserver 110 to perform the temporary traffic control 170-1.

It should be noted that the above descriptions of the applicationscenario are intended to be convenient, and one or more embodiments ofthe present disclosure may not be limited to the scope of thedisclosure. For those skilled in the art, various modifications orchanges may be made based on the description of the present disclosure.For example, the application scenario may also include a database. Forexample, the application scenario may be implemented on other devices toachieve similar or different functions. However, changes andmodifications do not depart from the scope of the present disclosure.

The IoT system is an information processing system including at leastone of an object platform, a sensor network platform, a managementplatform, a service platform, or an user platform. The managementplatform may realize the overall planning and coordination of theconnection and cooperation between various functional platforms (such asthe sensor network platform and the object platform), gather informationof the IoT operation system, and provide functions of perceptionmanagement and control management for the IoT operation system. Thesensor network platform may realize a connection between the managementplatform and the object platform to play sensing communication functionsof perception information and control information. The object platformmay be a functional platform for generating the perceptual informationand executing the control information. The service platform may refer toa platform which provides input and output services for users. The userplatform may refer to a platform dominated by users, which includes aplatform for obtaining user needs and feeding information back to theusers.

The information processing flow in the IoT system may be divided into aprocessing flow of the perception information and a processing flow ofthe control information. The control information may be informationgenerated based on the perception information. The processing flow ofthe perceptual information may include obtaining the perceptualinformation by the object platform and transmitting the perceptualinformation to the management platform through the sensor networkplatform. The processing flow of the control information may includedelivering the control information to the object platform through thesensor network platform so as to realize control of correspondingobject.

In some embodiments, when the IoT system is applied to city management,it may be referred to as an IoT system in a smart city.

FIG. 2 is a schematic diagram illustrating an exemplary IoT system forcontrolling a traffic scheduling strategy in a smart city according tosome embodiments of the present disclosure.

The user platform may refer to a platform dominated by users, whichincludes a platform for obtaining user needs and feeding informationback to the users. For example, the user platform may obtain user'sinput instruction and query the traffic data information through theuser terminal (e.g., the user terminal 130). As another example, theuser platform may obtain user's control instruction through the userterminal to control the road monitoring device (e.g., the roadmonitoring device 140-1) and/or the UAV shooting device (e.g., the UAVshooting device 140-2). As another example, the user platform may feedtraffic scheduling results back to the users.

The service platform may refer to a platform which provides input andoutput services for users. For example, the service platform may obtainquery instructions issued by the user through the user platform to querythe traffic data information and feed the traffic data information backto the user. In some embodiments, the service platform may include aservice information management platform and a plurality of servicesub-platforms.

The traffic scheduling strategy control management platform may be aplatform for overall planning and coordination of the connection andcooperation between various functional platforms, gathering allinformation of the IoT operation system, and provide functions ofperception management and control management for the IoT operationsystem. For example, the traffic scheduling strategy control managementplatform may obtain the traffic data information in a preset area duringa current time period and predict one or more target areas where trafficcongestion is likely to occur in the preset area during a next timeperiod based on the traffic data information in the preset area duringthe current time period. For another example, the traffic schedulingstrategy control management platform may determine whether the trafficscheduling strategy is needed to be switched based on the traffic datainformation in the one or more target areas during the next time period.In response to determining that the traffic scheduling strategy isneeded to be switched, the traffic scheduling strategy controlmanagement platform may switch a first traffic scheduling strategy to asecond traffic scheduling strategy.

In some embodiments, the traffic scheduling strategy control managementplatform may include an information management platform and a pluralityof management sub-platforms.

In some embodiments, the traffic scheduling strategy control managementplatform may include one or more management sub-platforms such as a roadcondition reporting management platform, a special vehicle managementplatform, a special transport management platform, and a road occupationconstruction management platform. Different management sub-platforms mayprovide information independently for the information managementplatform through different management platform sub databases. Forexample, the road condition reporting management platform may provideroad condition information for the information management platformthrough a road condition reporting database. The special vehiclemanagement platform may provide special vehicle information (such asquantity, duty status, etc.) for the information management platformthrough a special vehicle database. The special transport managementplatform may provide special transport information (such as quantity,travel time, location, etc.) for the information management platformthrough a special transport database. The road occupation constructionmanagement platform may provide road occupation construction information(such as locations of road occupation construction, estimated time ofroad occupation, etc.) for the information management platform through aroad occupation construction database. The information managementplatform may comprehensively manage the obtained information and sendthe obtained information to the service platform according to the userneeds.

The sensor network platform may refer to a functional platform forrealizing a connection between the traffic scheduling strategy controlmanagement platform and the object platform to play sensingcommunication functions of perception information and controlinformation. In some embodiments, the sensor network platform may beconfigured as an IoT gateway (e.g., an IoT gateway 150), which may beused to establish a channel for uploading the perception information anddelivering the control information between the user terminal (e.g., theuser terminal 130) and/or the sensing device (e.g., the sensing device140) and the traffic scheduling strategy control management platform. Insome embodiments, the sensor network platform may include a plurality ofsensor network sub-platforms, and the plurality of sensor networksub-platforms may be a plurality of sensor network sub-platformscorresponding to different object platforms. In some embodiments, eachof the plurality of the sensor network sub-platforms may be configuredas an IoT gateway in different communication networks (e.g., an IoTgateway of the road monitoring device 150-1, an IoT gateway of the UAVshooting device 150-2, and an IoT gateway of the user terminal 150-3).Each of the plurality of the sensor network sub-platforms may processthe monitoring data uploaded by the road monitoring device, the UAVshooting device, and the user terminal and store the processedmonitoring data to the sensor network platform database, so as todistribute the monitoring data to different sensor network platform subdatabases for processing and storage. The processed monitoring data maybe collected and stored in the sensor network platform database andtransmitted to a sensor information management platform, and theprocessed monitoring data may be uniformly transmitted to the trafficscheduling strategy control management platform and stored in themanagement platform database from the sensor information managementplatform.

The object platform may refer to a functional platform for generatingthe perception information and executing ultimately the controlinformation. In some embodiments, the object platform may be configuredas a road monitoring device, a UAV shooting device, and a user terminal.In some embodiments, the object platform may be classified into multipleobject sub-platforms based on different types of sensing devices. Forexample, the object platform may be classified into road monitoringdevice platforms including one or more road monitoring devices based onroad monitoring device, the object platform may be classified into UAVshooting device platforms including one or more UAV shooting devicesbased on the UAV shooting device, and the object platform may beclassified into user terminal platforms including one or more userterminals based on the user terminal.

The sub-platforms may refer to a part of the platforms classified basedon types of task. In some embodiments, the service platform, the trafficscheduling strategy control management platform, the sensor networkplatform, and the object platform may all be provided with multiplesub-platforms according to needs. The sub-platforms may assist theplatforms to process information more efficiently and solve the problemof insufficient computing power of the platforms.

The database may refer to a collection of stored data. For example, themanagement platform database may store the data information of roadcondition reporting, special vehicles, special transportation, occupyingroad construction.

The sub database may refer to a partial data set of the databaseclassified based on data type. In some embodiments, the service platformdatabase, the management platform database, and the sensor networkplatform database may all be provided with multiple sub databasesaccording to needs.

In some embodiments, the management platform database may obtain thetraffic data information based on the object platform, and themanagement sub-platform database may obtain the traffic data informationbased on the management platform database.

In some embodiments, the management platform database may obtain thetraffic data information through the sensing information managementplatform and the sensor network platform database based on the objectplatform.

In some embodiments, the management platform database may obtain thespecial vehicle driving route information from the road monitoringdevice through the sensor information management platform and the sensornetwork platform database and upload the special vehicle driving routeinformation to the special vehicle database corresponding to the specialvehicle management platform.

The controlling of traffic scheduling strategy in a smart city may beimplemented through IoT functional architectures of the above platforms,which realizes a closed-loop of an information process to process theIoT information more fluently and efficiently.

FIG. 3 is a flowchart illustrating an exemplary process of a method forcontrolling a traffic scheduling strategy in a smart city according tosome embodiments of the present disclosure. As shown in FIG. 3 , theprocess 300 may include the following steps. In some embodiments, theprocess 300 may be executed by the processing device 112.

In step 310, the process device 112 may obtain traffic data informationin a preset area during a current time period, and the traffic datainformation may at least include a speed of at least one vehicle on atleast one road.

The preset area may refer to an area set in advance. In someembodiments, the preset area may include a city, a district, a street,or the like. For example, the preset area may be Beijing. For example,the preset area may be Chaoyang District, Beijing. For example, thepreset area may be Chaoyang North Road, Chaoyang District, Beijing.

The current time period may be a time period when the traffic datainformation is obtained. For example, the current time period may befrom 18:00 on Jan. 1, 2032 to 18:10 on Jan. 1, 2032.

The traffic data information during the current time period may includedata information reflecting traffic conditions during the current timeperiod. In some embodiments, the traffic data information during thecurrent time period may include information, such as road conditioninformation, special vehicle information, special transport information,road occupation construction information, etc., reported by the userduring the current time period In some embodiments, the traffic datainformation during the current time period may include vehicle features(e.g., a number of vehicles, vehicle types, etc.) obtained by the roadmonitoring device or the UAV shooting device. In some embodiments, thetraffic data information during the current time period may at leastinclude the speed of at least one vehicle on at least one road duringthe current time period obtained by the road monitoring device. Thespeed of at least one vehicle may refer to an average speed of at leastone vehicle on at least one road.

In some embodiments, the processing device may obtain the traffic datainformation based on video or image information provided by the objectplatform (e.g., the sensing device 140). In some embodiments, theprocessing device may obtain the traffic data information based oninformation of the road conditions, special vehicles, specialtransportation, and occupying road construction reported by the objectplatform (e.g., the user terminal 130).

In some embodiments, the management platform database may obtain thetraffic data information based on the object platform, and themanagement sub-platform database may obtain the traffic data informationbased on the management platform database. More descriptions regardingobtaining the traffic data information in the preset area during thecurrent time period may be found elsewhere in the present disclosure,for example, FIG. 2 and its relevant descriptions thereof.

In step 320, the process device 112 may predict one or more target areaswhere traffic congestion is likely to occur in the preset area during anext time period based on the traffic data information in the presetarea during the current time period.

The next time period may be a time period after the current time period(for example, next 5 minutes, 10 minutes, 30 minutes, etc.). Forexample, if the current time period may be from 18:00 on Jan. 1, 2032 to18:10 on Jan. 1, 2032, and the next time period may be from 18:10 onJan. 1, 2032 to 18:20 on Jan. 1, 2032.

The traffic congestion may refer to a phenomenon where vehicles arecrowded and moving slowly. For example, if a traffic accident occurs onthe road corresponding to a high-speed entrance resulting in trafficcongestion, a large number of vehicles may flow into other adjacententrances of the high-speed entrance, causing traffic congestion ofother roads, intersections, or entrances. For another example, vehiclesmay take a detour in a certain place with a traffic accident, occupyingroad construction, special vehicle traffic, special transportation, etc.In this case, the vehicles will pass through other roads (e.g., adjacentroads), which is bound to affect traffic of other roads.

The target area may refer to an area where traffic congestion is likelyto occur in the preset area during the next time period. In someembodiments, the preset area may include a certain location,intersection, or the like. For example, the target area may be anintersection of Chaoyang North Road and Binhe Road, Chaoyang District,Beijing. For example, the target area may be a West Gate of TsinghuaUniversity. When a node connected to a road is predicted as the targetarea, the road may be considered to be congested. Therefore, thepredicted target area may reflect the traffic congestion condition.

In some embodiments, the processing device may predict the target areain a variety of ways.

In some embodiments, the processing device may input the traffic datainformation during the current time period into a traffic stateprediction model, and the traffic state prediction model may output oneor more target areas where traffic congestion is likely to occur in thepreset area during the next time period.

The traffic state prediction model may be a deep learning model, such asa Graph Neural Network (GNN) model, etc. More descriptions about thetraffic state prediction model may be found elsewhere in the presentdisclosure (for example, FIG. 4 and its relevant descriptions thereof).

In step 330, the processing device may determine whether the trafficscheduling strategy is needed to be switched based on the traffic datainformation in the one or more target areas during the next time period.

The traffic data information during the next time period may includedata information reflecting traffic conditions during the next timeperiod. In the embodiment, the traffic data information during the nexttime period may at least include the speed of the at least one vehicleon at least one road during the next time period.

In some embodiments, the processing device may use a first model toprocess the traffic data information during the current time period toobtain the traffic data information in the one or more target areasduring the next time period. For example, the processing device mayinput the traffic data information during the current time period to thefirst model, and the first model may output the traffic data informationin the one or more target areas during the next time period. The firstmodel may include a deep neural network (DNN), a recurrent neuralnetwork (RNN), a convolutional neural network (CNN), or the like. Thefirst model may be trained by the processing device using the trafficdata information during a historical current period as training data, sothat the first model may output the traffic data information in the oneor more target areas during the historical next period based on thetraffic data information during the current time period. The labelcorresponding to the training data may be determined by the historicaldata.

The traffic scheduling strategy may refer to a strategy to maintaintraffic order in the preset area. In some embodiments, the trafficscheduling strategy may include one or more of a number of trafficpolice scheduled, duration of traffic lights, and temporary trafficcontrol.

The number of traffic police scheduled may refer to a number of trafficpolice needed to be scheduled. For example, if a road occurs serioustraffic congestion due to an accident, five traffic policemen need to bescheduled for on-site management.

The duration of traffic lights may refer to signal duration of redlight, green light, and/or yellow light. For example, when a specialvehicle passes through an intersection, in order to ensure passage ofspecial vehicles, the duration of green light will be extended in thisdirection and the duration of red light will be extended in the otherdirection.

The temporary traffic control may refer to measures of prohibiting andrestricting traffic in violation of daily traffic rules. For example,the processing device may implement prohibiting two-way passage of thetemporary traffic control on the road due to the front road occupationconstruction.

In some embodiments, it may be determined that the traffic schedulingstrategy is needed to be switched in the target area when a reducedvalue or an increased value of a speed of a vehicle in the target areaduring the next time period relative to a speed of a vehicle in thetarget area during the current time period is greater than or equal to apreset threshold. The reduced value or the increased value of the speedof the vehicle may be obtained based on the speed of the vehicle in thetarget area during the next time period minus the speed of the vehiclein the target area during the current time period. The speed of thevehicle in the target area during the next time period may be obtainedbased on the traffic data information during the next time period, andthe speed of the vehicle during the current time period in the targetarea may be obtained based on the traffic data information during thecurrent time period. For example, assuming that the speed of the vehiclein the target area during the current time period is 40 km/h and thepreset threshold is 10 km/h, the processing device may determine thatthe traffic scheduling strategy is needed to be switched in the targetarea when the speed of the vehicle in the target area during the nexttime period is 25 km/h (the reduced value is 15 km/h, which is greaterthan the preset threshold); the processing device may determine that thetraffic scheduling strategy does not need to be switched in the targetarea when the speed of the vehicle in the target area during the nexttime period is 45 km/h (the increased value is 5 km/h, which is lessthan the preset threshold).

In step 340, in response to determining that the traffic schedulingstrategy is needed to be switched, the processing device may switch afirst traffic scheduling strategy to a second traffic schedulingstrategy.

The first traffic scheduling strategy may refer to a traffic schedulingstrategy in the preset area during the current time period. For example,the first traffic scheduling strategy in the preset area may be that thenumber of traffic police scheduled is 0, the duration of both red lightand green light are 30 seconds, and the temporary traffic control is notperformed in a first target area; the number of traffic police scheduledis 2, the duration of both red light and green light are 30 seconds, andthe temporary traffic control is not performed in a second target area.

The second traffic scheduling strategy may refer to a traffic schedulingstrategy in the preset area during the next time period. For example,assuming that the first traffic scheduling strategy in the preset areais that the number of traffic police scheduled is 0, the duration ofboth red light and green light are 30 seconds, and the temporary trafficcontrol is not performed in the first target area; the number of trafficpolice scheduled is 2, the duration of both red light and green lightare 30 seconds, and the temporary traffic control is not performed inthe second target area, the processing device may determine that thetraffic scheduling strategy is needed to be switched in both the firsttarget area and the second target area. If it is detected that anoccupying road construction occurs in the first target area and anaccident occurs in the second target area, the second traffic schedulingstrategy in the preset area may be that the number of traffic policescheduled is 3 and no thoroughfare of the temporary traffic control isperformed in the first target area; the number of traffic policescheduled is 5 and the duration of red light is adjusted to 60 secondsand the duration of green light is adjusted to 15 seconds in a predictedcongestion direction, and the temporary traffic control is not performedin the second target area.

In some embodiments, the processing device may switch the first trafficscheduling strategy to the second traffic scheduling strategy in avariety of ways.

In some embodiments, the processing device may switch the first trafficscheduling strategy to the second traffic scheduling strategy bydetermining the traffic data information (e.g., the speed of the vehiclein the target area) of the vehicle in the target area during the currenttime period and the next time period. For example, each target area mayinclude several speed ranges (for example, a speed limit in the targetarea is 60 km/h, a first speed range is 0-5 km/h, a second speed rangeis 5-10 km/h, a third speed range is 10-15 km/h, a fourth speed range is15-20 km/h, a fifth speed range is 20-25 km/h, a sixth speed range is25-30 km/h, a seventh speed range is 30-35 km/h, an eighth speed rangeis 35-40 km/h is, a ninth speed range is 40-45 km/h, a tenth speed rangeis 45-60 km/h) and the corresponding traffic scheduling strategies maybe stored in a same database. The corresponding traffic schedulingstrategies may include following scheduling strategies. For example,when the speed of the vehicle in the target area is in the tenth speedrange, which indicates that the target area is unobstructed withoutcongestion, the corresponding scheduling strategy may be that theduration of red light is not needed to extend, the number of trafficpolice scheduled is 0, and the temporary traffic control is notperformed; when the speed of the vehicle in the target area is in theeighth speed range, which indicates the target area is slightlycongested, the corresponding scheduling strategy is that the duration ofred light signal is needed to extend to 40 seconds, the number of thetraffic police scheduled is 0, and the temporary traffic control is notperformed; when the speed of the vehicle in the target area is in thefifth speed range, which indicates the target area is generalcongestion, the corresponding scheduling strategy is that the durationof red light is needed to extend to 60 seconds, the number of trafficpolice scheduled is 1, and the temporary traffic control is notperformed; when the speed of the vehicle in the target area is in thefirst speed range, which indicates that the target area is verycongested, the corresponding scheduling strategy is that the duration ofred light is not needed to extend, the number of traffic policescheduled is 5, and no thoroughfare of the traffic control is performed.The processing device may switch the first traffic scheduling strategyto the second traffic scheduling strategy through the database based onthe traffic data information in one or more target areas during thecurrent time period and the next time period. For example, if anintersection is generally congested during the current time period, thescheduling strategy of the intersection in the first traffic schedulingstrategy may be that the duration of traffic lights is needed to extendto 60 seconds, the number of traffic police scheduled is 1, and thetemporary traffic control is not performed. It is predicted that theintersection will be unobstructed during the next time period, so thescheduling strategy of the intersection in the second traffic schedulingstrategy may be switched to a scheduling strategy, the schedulingstrategy is that the duration of red light is not needed to extend, thenumber of traffic police scheduled is 0, and the temporary trafficcontrol is not performed. For another example, if an intersection isgenerally congested during the current time period, the schedulingstrategy of the intersection in the first traffic scheduling strategymay be that the duration of traffic lights is needed to extend to 60seconds, the number of traffic police scheduled is 1, and the temporarytraffic control is not performed. It is predicted that the intersectionwill be very congested during the next time period, so the schedulingstrategy of the intersection in the second traffic scheduling strategymay be switched to a scheduling strategy, the scheduling strategy isthat the duration of red light is not needed to extend, the number oftraffic police scheduled is 5, and no thoroughfare of the trafficcontrol is not performed.

In some embodiments, the processing device may input the first trafficscheduling strategy and the traffic data information in the one or moretarget areas during the current time period and the next time periodinto the traffic scheduling strategy prediction model, and then thesecond traffic scheduling strategy may be output by the trafficscheduling strategy prediction model.

The traffic scheduling strategy prediction model may be a depth learningmodel, for example, DNN, RNN, CNN, etc. More description about thetraffic scheduling strategy prediction model may be found elsewhere ofthe present disclosure thereof (for example, FIG. 5 and its relevantdescriptions).

The first traffic scheduling strategy is switched to the second trafficscheduling strategy, which can reduce blindness and dependence onexperience of traffic managers in formulating traffic schedulingstrategies, improve scientificity, accuracy, and timeliness of trafficstrategy determination method at a certain extent, so as to improveefficiency and quality of traffic management.

It should be noted that the above descriptions of the relevant processof the method for controlling the traffic scheduling strategy in a smartcity are intended to be convenient, and one or more embodiments of thepresent disclosure may not be limited to the scope of the disclosure.For those skilled in the art, various modifications and changes may bemade to the method for controlling the traffic scheduling strategy ofthe smart city under the guidance of the present disclosure. Thosemodifications and changes may be within the scope of the protection ofone or more embodiments of the disclosure.

FIG. 4 is a schematic diagram illustrating an exemplary structure 400 ofa traffic state prediction model according to some embodiments of thepresent disclosure.

In some embodiments, as shown in FIG. 4 , an input of the traffic stateprediction model 420 may include the traffic data information 410-1during the current time period and an output of the traffic stateprediction model 420 may be one or more target areas 430.

In some embodiments, the GNN model may process graph data constructedbased on a relationship of intersections of each road to determine theone or more target areas 430. In some embodiments, the graph may includea plurality of nodes and edges, the nodes correspond to theintersections of each road, and the edges correspond to a relationshipbetween the road connections. In some embodiments, the edges correspondto a spatial position relationship between roads, and the spatialposition relationship may be a relative position relationship, adistance relationship, etc. In some embodiments, the nodes and edges mayinclude their respective features, respectively. In some embodiments, anode feature may include whether there are traffic lights at theintersection of each road and the duration of traffic lights. An edgefeature may include a lane type corresponding to the road, a number oflanes, and whether there is an underpass tunnel.

In some embodiments, as shown in FIG. 4 , parameters of the trafficstate prediction model 420 may be trained by a plurality of labeledfirst training samples 440. In some embodiments, the processing devicemay obtain a plurality of groups of first training samples 440, and eachgroup of first training samples 440 may include a plurality of trainingdata and labels corresponding to the training data. The training datamay include historical traffic data information. The historical trafficdata information may be traffic data information during the historicaltime period. The labels of training data may be the one or more targetareas where traffic congestion is likely to occur in the preset areaduring a historical next time period, which is determined based on thehistorical traffic data information.

Parameters of an initial traffic state prediction model 450 may beupdated to obtain a trained traffic state prediction model 420 throughthe plurality of groups of first training samples 440.

In some embodiments, the processing device may iteratively update theparameters of the initial traffic state prediction model 450 to make aloss function of the initial traffic state prediction model meet apreset condition based on the plurality of groups of first trainingsamples. For example, the loss function converges, or a value of theloss function is less than a preset value. When the loss function meetsthe preset condition, a training process of the initial traffic stateprediction model is completed to obtain a trained initial traffic stateprediction model 450. The traffic state prediction model 420 and thetrained initial traffic state prediction model 450 may have a samestructure.

In some embodiments, a node feature of the traffic state predictionmodel 420 may also include at least one of a first strategy adjustmentfeature 410-2 or a second strategy adjustment feature 410-3.

The first strategy adjustment feature 410-2 may refer to adjusting theone or more target areas 430 based on the speed of the vehicle in thetarget area.

In some embodiments, the processing device may obtain the speed of thevehicle in the target area and adjust the target area based on the speedof the vehicle in the target area.

In some embodiments, the processor may obtain the speed of the vehiclein the target area based on the road monitoring video.

In some embodiments, the processor may adjust the target area based onthe speed of the vehicle in the target area. For example, if an area ispredicted as a target area due to the slow driving of vehicles, thetraffic condition will return to a normal condition (not congested)after the vehicles leave. In this case, it may be considered thattraffic congestion will not occur in this area during the next timeperiod, so this area is not predicted as the target area.

In some embodiments, the processing device may determine a first averagespeed of each of a plurality of vehicles before the current time on theroad connected with the target area based on the road monitoring videoin the target area before the current time. Next, the processing devicemay determine a number of first vehicles, the first average speeds ofwhich are less than a first preset threshold. When the number of firstvehicles is greater than a second preset threshold, the processingdevice may obtain the road monitoring video in the target area at thecurrent time, obtain a second average speed of each of the plurality ofvehicles at the current time based on the road monitoring video at thecurrent time, determine a number of second vehicles, the second averagespeeds of which are less than the first preset threshold. Finally, theprocessing device may compare the number of the first vehicles with thenumber of the second vehicles and adjust the target area based on acomparison result. For example, if a reduced value between the number ofsecond vehicles and the number of first vehicles is greater than a thirdpreset threshold, it is considered that the traffic congestion in thetarget area is caused by slow driving of individual vehicles. Withdeparture of individual vehicles in an area, the traffic will no longerbe congested, so this area is not predicted as the target area.

The second strategy adjustment feature 410-3 may refer to adjusting oneor more target areas 430 based on a change rate of vehicle flow in thetarget area.

The change rate of vehicle flow is a value reflecting a speed of vehicleflow change, which may also be referred to as vehicle flow acceleration.The change rate of vehicle flow, for example, may reflect a change speedof increase or decrease of vehicle flow.

In some embodiments, the processing device may determine a number ofvehicles in continuous multi frame images through a second model basedon continuous multi frame images collected by the sensor device andperform a linear fitting based on the number of vehicles in thecontinuous multi frame images. A slope of the fitting curve may be thechange rate of vehicle flow. Independent variables of the fitting curvemay include time of multi frames and dependent variables of the fittingcurve may include number of vehicles corresponding to the time of multiframes.

In some embodiments, the processing device may use the second model toprocess the image collected by the sensing device to obtain a number ofvehicles on the image. For example, the processing device may input theimage collected by the sensor device into the second model, and thenumber of vehicles on the image may be output by the second model. Thesecond model may include DNN, RNN, CNN, or the like. The processingdevice may use historical image as training data to train the secondmodel, so that the second model may output the number of vehicles on thehistorical image based on the historical image. The label correspondingto the training data may be determined manually.

In some embodiments, the processing device may obtain the change rate ofvehicle flow in the target area based on the road monitoring video.

In some embodiments, the processing device may obtain the change rate ofvehicle flow in the target area and adjust the target area based on thechange rate of vehicle flow in the target area. For example, if an areais not predicted as a target area, but its vehicle flow increasesrapidly, it indicates that there may be a large influx of vehicles in ashort time. In this case, it is likely to cause traffic congestion.Therefore, this area is predicted as a target area.

In some embodiments, the processing device may preset a time periodthreshold (e.g., 5 minutes, 10 minutes) and a change rate threshold ofvehicle flow. If the change rate of vehicle flow during the time periodis greater than the change rate threshold of vehicle flow, the targetarea is adjusted (for example, a non-target area is predicted as atarget area).

The target area predicted by the traffic state prediction model mayinclude using the traffic data information during the current timeperiod as an input of the traffic state prediction model and combiningwith interrelated prediction results of the first strategy adjustmentfeature or the second strategy adjustment feature, so as to makeprediction of the target area by the traffic state prediction model moreaccurate.

FIG. 5 is a schematic diagram illustrating an exemplary structure 500 ofa traffic scheduling strategy prediction model according to someembodiments of the present disclosure.

In some embodiments, as shown in FIG. 5 , an input of the trafficscheduling strategy prediction model 520 may include a first trafficscheduling strategy 510-1, the traffic data information 510-2 in the oneor more target areas during the current time period, and the trafficdata information 510-3 in the one or more target areas during the nexttime period. In some embodiments, an output of the traffic schedulingstrategy prediction model 520 may be a second traffic schedulingstrategy 530.

In some embodiments, as shown in FIG. 5 , parameters of the trafficscheduling strategy prediction model 520 may be trained by a pluralityof labeled second training samples 540. In some embodiments, theprocessing device may obtain a plurality of groups of second trainingsamples 540 based on a large amount of historical data. Each group ofthe second training samples 540 may include a plurality of training dataand labels corresponding to the plurality of training data. The trainingdata may include a historical first traffic scheduling strategy, trafficdata information in one or more target areas during a historical timeperiod, and traffic data information in the one or more target areasduring the historical next time period. The labels of the training datamay be a historical second traffic scheduling strategy manuallydetermined based on actual data.

Parameters of an initial traffic scheduling strategy prediction model550 may be updated to obtain a trained initial traffic schedulingstrategy prediction model 550 based on a plurality of groups of secondtraining samples 540. The parameters of the traffic scheduling strategyprediction model 520 are from the trained initial traffic schedulingstrategy prediction model 550.

In some embodiments, the processing device may iteratively update theparameters of the initial traffic scheduling strategy prediction model550 based on the plurality of groups of second training samples to makea loss function of the initial traffic scheduling strategy predictionmodel meet a preset condition. For example, the loss function converges,or the value of loss function is less than the preset value. When theloss function meets the preset condition, a training process of theinitial traffic scheduling strategy prediction model is completed toobtain a trained initial traffic scheduling strategy prediction model550. The traffic scheduling strategy prediction model 520 and thetrained initial traffic scheduling strategy prediction model 550 have asame model structure.

The first traffic scheduling strategy is switched to the second trafficscheduling strategy through the traffic scheduling strategy predictionmodel, and the first traffic scheduling strategy and the traffic datainformation during the current time period and the next time period inthe target area are input into the traffic scheduling strategyprediction model to obtain the second traffic scheduling strategy, whichsignificantly improves accuracy of the second traffic schedulingstrategy, so as to effectively reduce traffic congestion.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and be intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, e.g., an installation on an existing server or mobiledevice.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method for traffic scheduling at anintersection in a smart city, executed by a traffic scheduling strategycontrol management platform, comprising: determining, based on a roadmonitoring video of the intersection in a preset area before a currenttime, a first average speed of each vehicle among a plurality ofvehicles on one or more roads connected with the intersection before thecurrent time; determining a number of first vehicles, wherein the firstaverage speed of each first vehicle is less than a first presetthreshold; obtaining the road monitoring video of the intersection atthe current time when the number of the first vehicles is greater than asecond preset threshold; obtaining, based on the road monitoring videoat the current time, a second average speed of each vehicle among theplurality of vehicles at the current time; determining a number ofsecond vehicles, wherein the second average speed of each second vehicleis less than the first preset threshold; determining whether trafficcongestion is likely to occur at the intersection during a next timeperiod based on a comparison result obtained by comparing the number ofthe first vehicles with the number of second vehicles; in response todetermining that the traffic congestion is likely to occur at theintersection during the next time period, determining whether a trafficscheduling strategy is needed to be switched based on traffic datainformation of the intersection during the next time period; and inresponse to determining that the traffic scheduling strategy is neededto be switched, switching a first traffic scheduling strategy to asecond traffic scheduling strategy.
 2. The method of claim 1, furthercomprising: obtaining, based on an object platform, the traffic datainformation through a management platform database, and obtaining, basedon the management platform database, the traffic data informationthrough a management sub-platform database.
 3. The method of claim 2,wherein the obtaining, based on an object platform, the traffic datainformation through a management platform database includes: obtaining,based on the object platform, the traffic data information through asensor information management platform and a sensor network platformdatabase by the management platform database, wherein the objectplatform is configured as a road monitoring device, an Unmanned AerialVehicle (UAV) shooting device, and a user terminal.
 4. The method ofclaim 2, wherein the obtaining, based on an object platform, the trafficdata information by a management platform database includes: obtainingspecial vehicle driving route information from the road monitoringdevice through a sensor information management platform and a sensornetwork platform database by the management platform database, anduploading the special vehicle driving route information to a specialvehicle database corresponding to a special vehicle management platformby the management platform database.
 5. The method of claim 1, whereinthe traffic scheduling strategy includes at least one of a number ofscheduled traffic police, duration of traffic lights, or temporarytraffic control.
 6. The method of claim 1, further comprising: inresponse to determining that the traffic congestion is not likely tooccur at the intersection during the next time period, obtaining achange rate of vehicle flow of the intersection based on the roadmonitoring video; and determining whether the traffic congestion islikely to occur at the intersection during the next time period based onthe change rate of vehicle flow of the intersection.
 7. The method ofclaim 6, wherein the obtaining a change rate of vehicle flow of theintersection based on the road monitoring video includes: determining anumber of vehicles in continuous multi frame images through a secondmodel based on the continuous multi frame images of the road monitoringvideo; and performing a linear fitting based on the number of vehiclesin the continuous multi frame images, and determining a slope of afitting curve as the change rate of vehicle flow, wherein independentvariables of the fitting curve include time of the continuous multiframe images, and dependent variables of the fitting curve include anumber of vehicles corresponding to the time of the continuous multiframe images.
 8. The method of claim 6, wherein the determining whetherthe traffic congestion is likely to occur at the intersection during thenext time period based on the change rate of vehicle flow of theintersection includes: determining whether the change rate of vehicleflow at the intersection is greater than a change rate threshold duringa preset time period; and in response to determining that the changerate of vehicle flow at the intersection is greater than a change ratethreshold, determining that the traffic congestion is likely to occur atthe intersection during the next time period.
 9. The method of claim 1,wherein the switching a first traffic scheduling strategy to a secondtraffic scheduling strategy includes: determining the second trafficscheduling strategy through a traffic scheduling strategy predictionmodel based on the first traffic scheduling strategy and the trafficdata information of the intersection during the current time period andthe next time period, wherein the traffic scheduling strategy predictionmodel is a deep learning model.
 10. An Internet of Things (IoT) systemfor traffic scheduling at an intersection in a smart city, comprising auser platform, a service platform, a traffic scheduling strategy controlmanagement platform, a sensor network platform, and an object platforminteracting in sequence, wherein the traffic scheduling strategy controlmanagement platform is configured to perform operations comprising:determining, based on a road monitoring video of the intersection in apreset area before a current time, a first average speed of each vehicleamong a plurality of vehicles on one or more roads connected with theintersection before the current time; determining a number of firstvehicles, wherein the first average speed of each first vehicle is lessthan a first preset threshold; obtaining the road monitoring video ofthe intersection at the current time when the number of the firstvehicles is greater than a second preset threshold; obtaining, based onthe road monitoring video at the current time, a second average speed ofeach vehicle among the plurality of vehicles at the current time;determining a number of second vehicles, wherein the second averagespeed of each second vehicle is less than the first preset threshold;determining whether traffic congestion is likely to occur at theintersection during a next time period based on a comparison resultobtained by comparing the number of the first vehicles with the numberof second vehicles; in response to determining that the trafficcongestion is likely to occur at the intersection during the next timeperiod, determining whether a traffic scheduling strategy is needed tobe switched based on traffic data information of the intersection duringthe next time period; and in response to determining that the trafficscheduling strategy is needed to be switched, switching a first trafficscheduling strategy to a second traffic scheduling strategy.
 11. The IoTsystem of claim 10, wherein the traffic scheduling strategy controlmanagement platform is further configured to perform operationsincluding: obtaining, based on the object platform, the traffic datainformation through a management platform database, and obtaining, basedon the management platform database, the traffic data informationthrough a management sub-platform database.
 12. The IoT system of claim11, wherein to obtain, based on an object platform, the traffic datainformation through a management platform database, the trafficscheduling strategy control management platform is further configured toperform operations including: obtaining, based on the object platform,the traffic data information through a sensor information managementplatform and a sensor network platform database by the managementplatform database, wherein the object platform is configured as a roadmonitoring device, an Unmanned Aerial Vehicle (UAV) shooting device, anda user terminal.
 13. The IoT system of claim 11, wherein to obtain,based on an object platform, the traffic data information through amanagement platform database, the traffic scheduling strategy controlmanagement platform is further configured to perform operationsincluding: obtaining special vehicle driving route information from theroad monitoring device through a sensor information management platformand a sensor network platform database by the management platformdatabase, and uploading the special vehicle driving route information toa special vehicle database corresponding to a special vehicle managementplatform by the management platform database.
 14. The IoT system ofclaim 10, wherein the traffic scheduling strategy includes at least oneof a number of scheduled traffic police, duration of traffic lights, ortemporary traffic control.
 15. The IoT system of claim 10, wherein thetraffic scheduling strategy control management platform is furtherconfigured to perform operations including: in response to determiningthat the traffic congestion is not likely to occur at the intersectionduring the next time period, obtaining a change rate of vehicle flow ofthe intersection based on the road monitoring video; and determiningwhether the traffic congestion is likely to occur at the intersectionduring the next time period based on the change rate of vehicle flow ofthe intersection.
 16. The IoT system of claim 15, wherein to obtain achange rate of vehicle flow of the intersection based on the roadmonitoring video, the traffic scheduling strategy control managementplatform is further configured to perform operations including:determining a number of vehicles in continuous multi frame imagesthrough a second model based on the continuous multi frame images of theroad monitoring video; and performing a linear fitting based on thenumber of vehicles in the continuous multi frame images, and determininga slope of a fitting curve as the change rate of vehicle flow, whereinindependent variables of the fitting curve include time of thecontinuous multi frame images, and dependent variables of the fittingcurve include a number of vehicles corresponding to the time of thecontinuous multi frame images.
 17. The IoT system of claim 15, whereinto determine whether the traffic congestion is likely to occur at theintersection during the next time period based on the change rate ofvehicle flow of the intersection, the traffic scheduling strategycontrol management platform is further configured to perform operationsincluding: determining whether the change rate of vehicle flow at theintersection is greater than a change rate threshold during a presettime period; and in response to determining that the change rate ofvehicle flow at the intersection is greater than a change ratethreshold, determining that the traffic congestion is likely to occur atthe intersection during the next time period.
 18. The IoT system ofclaim 10, wherein to switch a first traffic scheduling strategy to asecond traffic scheduling strategy, the traffic scheduling strategycontrol management platform is further configured to perform operationsincluding: determining the second traffic scheduling strategy through atraffic scheduling strategy prediction model based on the first trafficscheduling strategy and the traffic data information of the intersectionduring the current time period and the next time period, wherein thetraffic scheduling strategy prediction model is a deep learning model.19. A non-transitory computer readable storage medium storing a set ofinstructions, when executed by at least one processor, causing at leastone processor to perform a method for traffic scheduling at anintersection in a smart city comprising: determining, based on a roadmonitoring video of the intersection in a preset area before a currenttime, a first average speed of each vehicle among a plurality ofvehicles on one or more roads connected with the intersection before thecurrent time; determining a number of first vehicles, wherein the firstaverage speed of each first vehicle is less than a first presetthreshold; obtaining the road monitoring video of the intersection atthe current time when the number of the first vehicles is greater than asecond preset threshold; obtaining, based on the road monitoring videoat the current time, a second average speed of each vehicle among theplurality of vehicles at the current time; determining a number ofsecond vehicles, wherein the second average speed of each second vehicleis less than the first preset threshold; determining whether trafficcongestion is likely to occur at the intersection during a next timeperiod based on a comparison result obtained by comparing the number ofthe first vehicles with the number of second vehicles; in response todetermining that the traffic congestion is likely to occur at theintersection during the next time period, determining whether a trafficscheduling strategy is needed to be switched based on traffic datainformation of the intersection during the next time period; and inresponse to determining that the traffic scheduling strategy is neededto be switched, switching a first traffic scheduling strategy to asecond traffic scheduling strategy.